In [1]:
import numpy             as np
import pandas            as pd
import scipy.stats       as stats
import seaborn           as sns
import matplotlib.pyplot as plt
import plotly.express    as px

Part A - 15 Marks¶

  1. Please refer the table below to answer below questions: [2 Marks]

1.A. Refer above table and find the joint probability of the people who planned to purchase and actually placed an order. [1 Mark]

In [2]:
# People who planned to purchase and actually placed an order = 400
# Total = 2000

output1 = (400/2000)

print('The joint probability of the people who planned to purchase and actually placed an order is %1.4f' % output1)

The joint probability of the people who planned to purchase and actually placed an order is 0.2000

1.B. Refer to the above table and find the joint probability of the people who planned to purchase and actually placed an order, given that people planned to purchase. [1 Mark]

In [3]:
# People who planned to purchase and actually placed an order = 400
# People who planned to purchase = 500
# Total = 2000

output2 = (400/2000) / (500/2000)

print('the joint probability of the people who planned to purchase and actually placed an order, given that people planned to purchase is %1.4f' % output2)

the joint probability of the people who planned to purchase and actually placed an order, given that people planned to purchase is 0.8000
  1. An electrical manufacturing company conducts quality checks at specified periods on the products it manufactures. Historically, the failure rate for the manufactured item is 5%. Suppose a random sample of 10 manufactured items is selected. Answer the following questions. [4 Marks]
In [4]:
# The failure rate for the manufactured item is 5%
# sample of manufactured items is selected is 10

failure_rate = (5/100)
sample_size = 10
k = np.arange(0, 12)

pmf = stats.binom.pmf(k, sample_size, failure_rate)
pmf
Out[4]:
array([5.98736939e-01, 3.15124705e-01, 7.46347985e-02, 1.04750594e-02,
       9.64808106e-04, 6.09352488e-05, 2.67259863e-06, 8.03789062e-08,
       1.58642578e-09, 1.85546875e-11, 9.76562500e-14, 0.00000000e+00])
In [5]:
cdf = stats.binom.cdf(k, sample_size, failure_rate)
cdf
Out[5]:
array([0.59873694, 0.91386164, 0.98849644, 0.9989715 , 0.99993631,
       0.99999725, 0.99999992, 1.        , 1.        , 1.        ,
       1.        , 1.        ])

2.A. Probability that none of the items are defective? [1 Mark]

In [6]:
print('Probability that none of the items are defective is %1.4f' % pmf[0])

Probability that none of the items are defective is 0.5987

2.B. Probability that exactly one of the items is defective? [1 Mark]

In [7]:
print('Probability that exactly one of the items is defective is %1.4f' % pmf[1])

Probability that exactly one of the items is defective is 0.3151

2.C. Probability that two or fewer of the items are defective? [1 Mark]

In [8]:
print('Probability that two or fewer of the items are defective is %1.4f' % cdf[2])

Probability that two or fewer of the items are defective is 0.9885

2.D. Probability that three or more of the items are defective ? [1 Mark]

In [9]:
print('Probability that two or fewer of the items are defective is %1.4f' % (1 - cdf[2]))

Probability that two or fewer of the items are defective is 0.0115
In [10]:
plt.plot(k, pmf, 'o-')
plt.title('Binomial Distribution')
plt.xlabel('Number of Defective Items')
plt.ylabel('Probability of Defective Items')
plt.show()
  1. A car salesman sells on an average 3 cars per week. [3 Marks]
In [11]:
# Sells on an average cars per week = 3

average_policies = 3
k = np.arange(0, 16)

poisson_cdf = stats.poisson.cdf(k, average_policies)
poisson_cdf
Out[11]:
array([0.04978707, 0.19914827, 0.42319008, 0.64723189, 0.81526324,
       0.91608206, 0.96649146, 0.9880955 , 0.99619701, 0.99889751,
       0.99970766, 0.99992861, 0.99998385, 0.9999966 , 0.99999933,
       0.99999988])
In [12]:
poisson_pmf = stats.poisson.pmf(k, average_policies)
poisson_pmf
Out[12]:
array([4.97870684e-02, 1.49361205e-01, 2.24041808e-01, 2.24041808e-01,
       1.68031356e-01, 1.00818813e-01, 5.04094067e-02, 2.16040315e-02,
       8.10151179e-03, 2.70050393e-03, 8.10151179e-04, 2.20950322e-04,
       5.52375804e-05, 1.27471339e-05, 2.73152870e-06, 5.46305740e-07])

3.A. What is Probability that in a given week he will sell some cars? [1 Mark]

In [13]:
print('Probability that in a given week he will sell some cars is %1.4f' % (1 - poisson_cdf[0]))

Probability that in a given week he will sell some cars is 0.9502

3.B. What is Probability that in a given week he will sell 2 or more but less than 5 cars? [1 Mark]

In [14]:
print('Probability that in a given week he will sell some cars is %1.4f' % (poisson_cdf[4] - poisson_cdf[1]))

Probability that in a given week he will sell some cars is 0.6161

3.C. Plot the poisson distribution function for cumulative probability of cars sold per-week vs number of cars sold per week. [1 Mark]

In [15]:
plt.plot(k, poisson_pmf, 'o-')
plt.title('Poisson Distribution')
plt.xlabel('Number of cars sold per-week')
plt.ylabel('Cumulative Probability of cars sold per-week')
plt.show()
  1. Accuracy in understanding orders for a speech based bot at a restaurant is important for the Company X which has designed, marketed and launched the product for a contactless delivery due to the COVID-19 pandemic. Recognition accuracy that measures the percentage of orders that are taken correctly is 86.8%. Suppose that you place an order with the bot and two friends of yours independently place orders with the same bot. Answer the following questions. [3 Marks]
In [16]:
# The percentage of orders that are taken correctly is 86.8%

orders_taken_correctly = (86.8/100)
sample_size = 3
k = np.arange(0, 12)

binomial_pmf = stats.binom.pmf(k, sample_size, orders_taken_correctly)
binomial_pmf
Out[16]:
array([0.00229997, 0.0453721 , 0.2983559 , 0.65397203, 0.        ,
       0.        , 0.        , 0.        , 0.        , 0.        ,
       0.        , 0.        ])
In [17]:
binomial_cdf = stats.binom.cdf(k, sample_size, orders_taken_correctly)
binomial_cdf
Out[17]:
array([0.00229997, 0.04767206, 0.34602797, 1.        , 1.        ,
       1.        , 1.        , 1.        , 1.        , 1.        ,
       1.        , 1.        ])

4.A. What is the probability that all three orders will be recognised correctly? [1 Mark]

In [18]:
print('Probability that all three orders will be recognised correctly is %1.4f' % binomial_pmf[3])
print('In percentage, %1.2f' % (binomial_pmf[3] * 100),'%')

Probability that all three orders will be recognised correctly is 0.6540
In percentage, 65.40 %

4.B. What is the probability that none of the three orders will be recognised correctly? [1 Mark]

In [19]:
print('Probability that none of the three orders will be recognised correctly is %1.4f' % binomial_pmf[0])
print('In percentage, %1.2f' % (binomial_pmf[0] * 100),'%')

Probability that none of the three orders will be recognised correctly is 0.0023
In percentage, 0.23 %

4.C. What is the probability that at least two of the three orders will be recognised correctly? [1 Mark]

In [20]:
print('Probability that at least two of the three orders will be recognised correctly is %1.4f' % (binomial_pmf[2] + binomial_pmf[3]))
print('In percentage, %1.2f' % ((binomial_pmf[2] + binomial_pmf[3]) * 100),'%')

Probability that at least two of the three orders will be recognised correctly is 0.9523
In percentage, 95.23 %
In [21]:
plt.plot(k, binomial_cdf, 'o-')
plt.title('Binomial Distribution')
plt.xlabel('Percentage of orders that are taken correctly')
plt.ylabel('Cumulative Probability of orders recognised correctly')
plt.show()
  1. Explain 1 real life industry scenario (other than the ones mentioned above) where you can use the concepts learnt in this module of Applied Statistics to get data driven business solution. [3 Marks]

Examples of Applied Statistics in Real Life

There are a variety of applications used in our daily life that tend to make use of applied statistics and related theories. Some of them are listed below:

  1. Stock Market data analysis Stock market analysis is a classic example of statistical analysis in real life. The investor or the consumer willing to invest in the market tends to take all the available data from the market and perform research and analysis on it with the help of various statistical models to determine the performance portfolio of different investments. This helps the user improve his/her chances of making the most appropriate choice of all the available options. To simplify this process, a variety of software, web pages, and mobile applications have been developed and are available over the internet to educate a person about the working of the stock market and to properly guide him/her throughout the process of making an investment.

  2. Weather Forecasting Weather Forecasting is yet another example of a real-life application that makes use of statistical analysis. Weather forecasting basically depends on predicting the probability of occurrence of a particular event based on a collection of past or historical data. To perform weather forecasting with utmost efficiency, the historical trends related to the weather and climate conditions such as air temperature, pressure value, magnitude of humidity, air quality index, the appearance of clouds, speed and direction of winds, precipitation levels and frequency, etc. are captured in form of sample datasets. The bunch of raw data is then fed to algorithms that perform the necessary computation and analysis to draw out conclusions. Weather forecasting basically falls under the category of inferential statistics.

Part B - 30 Marks¶

• DOMAIN: Sports

• CONTEXT: Company X manages the men's top professional basketball division of the American league system. The dataset contains information on all the teams that have participated in all the past tournaments. It has data about how many baskets each team scored, conceded, how many times they came within the first 2 positions, how many tournaments they have qualified, their best position in the past, etc.

• DATA DESCRIPTION: Basketball.csv - The data set contains information on all the teams so far participated in all the past tournaments.

• DATA DICTIONARY:

  1. Team: Team’s name
  2. Tournament: Number of played tournaments.
  3. Score: Team’s score so far.
  4. PlayedGames: Games played by the team so far.
  5. WonGames: Games won by the team so far.
  6. DrawnGames: Games drawn by the team so far.
  7. LostGames: Games lost by the team so far.
  8. BasketScored: Basket scored by the team so far.
  9. BasketGiven: Basket scored against the team so far.
  10. TournamentChampion: How many times the team was a champion of the tournaments so far.
  11. Runner-up: How many times the team was a runners-up of the tournaments so far.
  12. TeamLaunch: Year the team was launched on professional basketball.
  13. HighestPositionHeld: Highest position held by the team amongst all the tournaments played.

• PROJECT OBJECTIVE: Company’s management wants to invest on proposals on managing some of the best teams in the league. The analytics department has been assigned with a task of creating a report on the performance shown by the teams. Some of the older teams are already in contract with competitors. Hence Company X wants to understand which teams they can approach which will be a deal win for them.

• STEPS AND TASK [30 Marks]:

  1. Read the data set, clean the data and prepare final dataset to be used for analysis. [10 Marks]
In [22]:
# Read the data set

basketball_df = pd.read_csv('Basketball.csv');
basketball_df
Out[22]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion Runner-up TeamLaunch HighestPositionHeld
0 Team 1 86 4385 2762 1647 552 563 5947 3140 33 23 1929 1
1 Team 2 86 4262 2762 1581 573 608 5900 3114 25 25 1929 1
2 Team 3 80 3442 2614 1241 598 775 4534 3309 10 8 1929 1
3 Team 4 82 3386 2664 1187 616 861 4398 3469 6 6 1931to32 1
4 Team 5 86 3368 2762 1209 633 920 4631 3700 8 7 1929 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
56 Team 57 1 34 38 8 10 20 38 66 - - 2009-10 20
57 Team 58 1 22 30 7 8 15 37 57 - - 1956-57 16
58 Team 59 1 19 30 7 5 18 51 85 - - 1951~52 16
59 Team 60 1 14 30 5 4 21 34 65 - - 1955-56 15
60 Team 61 1 - - - - - - - - - 2017~18 9

61 rows × 13 columns

In [23]:
# Shape the data set

basketball_df.shape
Out[23]:
(61, 13)
In [24]:
# Information on data and datatypes

basketball_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 13 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   Team                 61 non-null     object
 1   Tournament           61 non-null     int64 
 2   Score                61 non-null     object
 3   PlayedGames          61 non-null     object
 4   WonGames             61 non-null     object
 5   DrawnGames           61 non-null     object
 6   LostGames            61 non-null     object
 7   BasketScored         61 non-null     object
 8   BasketGiven          61 non-null     object
 9   TournamentChampion   61 non-null     object
 10  Runner-up            61 non-null     object
 11  TeamLaunch           61 non-null     object
 12  HighestPositionHeld  61 non-null     int64 
dtypes: int64(2), object(11)
memory usage: 6.3+ KB
In [25]:
# Clean the data

# Check for null values

basketball_df.isna().sum()
Out[25]:
Team                   0
Tournament             0
Score                  0
PlayedGames            0
WonGames               0
DrawnGames             0
LostGames              0
BasketScored           0
BasketGiven            0
TournamentChampion     0
Runner-up              0
TeamLaunch             0
HighestPositionHeld    0
dtype: int64

No Null values in the data set

In [26]:
# Check for duplicates in data

sum(basketball_df.duplicated())
Out[26]:
0

No Duplicate values in the data set

In [27]:
# Replace the Nan values with zero in the dataframe

basketball_df.replace(np.nan, 0, inplace=True)
In [28]:
basketball_df.describe(include = "all").T
Out[28]:
count unique top freq mean std min 25% 50% 75% max
Team 61 61 Team 1 1 NaN NaN NaN NaN NaN NaN NaN
Tournament 61.0 NaN NaN NaN 24.0 26.827225 1.0 4.0 12.0 38.0 86.0
Score 61 61 4385 1 NaN NaN NaN NaN NaN NaN NaN
PlayedGames 61 53 2762 3 NaN NaN NaN NaN NaN NaN NaN
WonGames 61 59 7 2 NaN NaN NaN NaN NaN NaN NaN
DrawnGames 61 57 14 2 NaN NaN NaN NaN NaN NaN NaN
LostGames 61 56 37 3 NaN NaN NaN NaN NaN NaN NaN
BasketScored 61 60 70 2 NaN NaN NaN NaN NaN NaN NaN
BasketGiven 61 61 3140 1 NaN NaN NaN NaN NaN NaN NaN
TournamentChampion 61 8 - 52 NaN NaN NaN NaN NaN NaN NaN
Runner-up 61 10 - 48 NaN NaN NaN NaN NaN NaN NaN
TeamLaunch 61 47 1929 10 NaN NaN NaN NaN NaN NaN NaN
HighestPositionHeld 61.0 NaN NaN NaN 7.081967 5.276663 1.0 3.0 6.0 10.0 20.0
In [29]:
basketball_df.tail()
Out[29]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion Runner-up TeamLaunch HighestPositionHeld
56 Team 57 1 34 38 8 10 20 38 66 - - 2009-10 20
57 Team 58 1 22 30 7 8 15 37 57 - - 1956-57 16
58 Team 59 1 19 30 7 5 18 51 85 - - 1951~52 16
59 Team 60 1 14 30 5 4 21 34 65 - - 1955-56 15
60 Team 61 1 - - - - - - - - - 2017~18 9
In [30]:
# Check for unique values for all the columns

print("Team", basketball_df['Team'].unique())
print('-----------------------------------------------------------------------------------');
print("Tournament", basketball_df['Tournament'].unique())
print('-----------------------------------------------------------------------------------');
print("Score", basketball_df['Score'].unique())
print('-----------------------------------------------------------------------------------');
print("PlayedGames", basketball_df['PlayedGames'].unique())
print('-----------------------------------------------------------------------------------');
print("WonGames", basketball_df['WonGames'].unique())
print('-----------------------------------------------------------------------------------');
print("DrawnGames", basketball_df['DrawnGames'].unique())
print('-----------------------------------------------------------------------------------');
print("LostGames", basketball_df['LostGames'].unique())
print('-----------------------------------------------------------------------------------');
print("BasketScored", basketball_df['BasketScored'].unique())
print('-----------------------------------------------------------------------------------');
print("BasketGiven", basketball_df['BasketGiven'].unique())
print('-----------------------------------------------------------------------------------');
print("TournamentChampion", basketball_df['TournamentChampion'].unique())
print('-----------------------------------------------------------------------------------');
print("Runner-up", basketball_df['Runner-up'].unique())
print('-----------------------------------------------------------------------------------');
print("TeamLaunch", basketball_df['TeamLaunch'].unique())
print('-----------------------------------------------------------------------------------');
print("HighestPositionHeld", basketball_df['HighestPositionHeld'].unique())

Team ['Team 1' 'Team 2' 'Team 3' 'Team 4' 'Team 5' 'Team 6' 'Team 7' 'Team 8'
 'Team 9' 'Team 10' 'Team 11' 'Team 12' 'Team 13' 'Team 14' 'Team 15'
 'Team 16' 'Team 17' 'Team 18' 'Team 19' 'Team 20' 'Team 21' 'Team 22'
 'Team 23' 'Team 24' 'Team 25' 'Team 26' 'Team 27' 'Team 28' 'Team 29'
 'Team 30' 'Team 31' 'Team 32' 'Team 33' 'Team 34' 'Team 35' 'Team 36'
 'Team 37' 'Team 38' 'Team 39' 'Team 40' 'Team 41' 'Team 42' 'Team 43'
 'Team 44' 'Team 45' 'Team 46' 'Team 47' 'Team 48' 'Team 49' 'Team 50'
 'Team 51' 'Team 52' 'Team 53' 'Team 54' 'Team 55' 'Team 56' 'Team 57'
 'Team 58' 'Team 59' 'Team 60' 'Team 61']
-----------------------------------------------------------------------------------
Tournament [86 80 82 73 70 58 51 45 42 44 43 37 36 38 27 33 17 23 21 12 20 13 18 11
 14  9  7  6  4  5  3  2  1]
-----------------------------------------------------------------------------------
Score ['4385' '4262' '3442' '3386' '3368' '2819' '2792' '2573' '2109' '1884'
 '1814' '1789' '1471' '1416' '1389' '1351' '1314' '1174' '1148' '1020'
 '970' '667' '662' '606' '553' '538' '510' '445' '421' '416' '375' '353'
 '343' '293' '285' '277' '242' '230' '190' '188' '168' '150' '148' '132'
 '107' '96' '91' '83' '81' '76' '71' '56' '52' '42' '40' '35' '34' '22'
 '19' '14' '-']
-----------------------------------------------------------------------------------
PlayedGames ['2762' '2614' '2664' '2408' '2626' '2302' '1986' '1728' '1530' '1698'
 '1466' '1428' '1458' '1318' '1255' '1192' '988' '1096' '646' '742' '652'
 '678' '456' '628' '494' '586' '380' '402' '423' '426' '448' '346' '334'
 '270' '228' '282' '160' '186' '204' '180' '152' '114' '130' '116' '80'
 '108' '90' '72' '68' '54' '38' '30' '-']
-----------------------------------------------------------------------------------
WonGames ['1647' '1581' '1241' '1187' '1209' '990' '948' '864' '698' '606' '563'
 '586' '463' '453' '471' '426' '390' '408' '333' '367' '266' '218' '189'
 '203' '147' '184' '155' '145' '125' '113' '123' '129' '104' '96' '103'
 '76' '62' '82' '52' '50' '59' '53' '37' '35' '43' '26' '34' '20' '19'
 '30' '29' '21' '17' '18' '13' '8' '7' '5' '-']
-----------------------------------------------------------------------------------
DrawnGames ['552' '573' '598' '616' '633' '531' '608' '577' '522' '440' '392' '389'
 '384' '336' '358' '327' '330' '292' '256' '242' '172' '175' '148' '180'
 '112' '149' '128' '143' '81' '95' '102' '127' '92' '79' '76' '56' '63'
 '45' '46' '50' '44' '37' '27' '21' '16' '23' '24' '13' '14' '18' '6' '11'
 '10' '8' '5' '4' '-']
-----------------------------------------------------------------------------------
LostGames ['563' '608' '775' '861' '920' '887' '1070' '766' '682' '575' '723' '619'
 '639' '629' '565' '535' '492' '399' '487' '208' '349' '305' '295' '197'
 '211' '298' '174' '194' '198' '202' '217' '158' '152' '118' '110' '137'
 '63' '90' '95' '83' '78' '52' '66' '44' '37' '62' '48' '33' '30' '41'
 '19' '20' '15' '18' '21' '-']
-----------------------------------------------------------------------------------
BasketScored ['5947' '5900' '4534' '4398' '4631' '3680' '3609' '3228' '2683' '2159'
 '2052' '2278' '1767' '1843' '1753' '1500' '1421' '1642' '1182' '1347'
 '892' '819' '760' '750' '520' '716' '619' '607' '458' '430' '422' '492'
 '393' '291' '419' '320' '244' '285' '199' '202' '216' '165' '155' '139'
 '227' '101' '181' '62' '70' '145' '121' '153' '71' '97' '36' '38' '37'
 '51' '34' '-']
-----------------------------------------------------------------------------------
BasketGiven ['3140' '3114' '3309' '3469' '3700' '3373' '3889' '3230' '2847' '2492'
 '2188' '2624' '2180' '2368' '2152' '1834' '1763' '1951' '1371' '1746'
 '789' '1157' '1088' '1022' '633' '1050' '744' '992' '623' '632' '581'
 '720' '662' '489' '588' '410' '366' '430' '241' '296' '310' '221' '253'
 '167' '308' '139' '295' '117' '115' '252' '183' '184' '116' '131' '182'
 '55' '66' '57' '85' '65' '-']
-----------------------------------------------------------------------------------
TournamentChampion ['33' '25' '10' '6' '8' '1' '-' '2']
-----------------------------------------------------------------------------------
Runner-up ['23' '25' '8' '6' '7' '4' '-' '3' '1' '5']
-----------------------------------------------------------------------------------
TeamLaunch ['1929' '1931to32' '1934-35' '1939-40' '1932-33' '1941to42' '1948-49'
 '1944_45' '1935-36' '1949_50' '1933to34' '1960-61' '1951-52' '1998-99'
 '1941-42' '1977-78' '1959-60' '2004to05' '1961-62' '1940-41' '1930-31'
 '1963-64' '1974-75' '1943-44' '1987-88' '1991_92' '2007-08' '1962-63'
 '1994-95' '1978-79' '1971-72' '1999to00' '2014-15' '1990-91' '1947-48'
 '1996-97' '1995-96' '1945-46' '1953-54' '1979-80' '1950-51' '2016_17'
 '2009-10' '1956-57' '1951~52' '1955-56' '2017~18']
-----------------------------------------------------------------------------------
HighestPositionHeld [ 1  3  2  4  6  8  5 11  7 12 10 17  9 19 14 16 20 15]

  1. There are special character '-' in multiple columns which needs to be eliminated as this is data missing.

  2. Column name 'Runner-up' contains special character '-' which needs to be renamed.

  3. Column name 'TeamLaunch' contains special character '~, _' and also 'to' which needs to be replaced.

  4. Column name 'Team' data set value contains space which needs to be removed and replace the value.

  5. Change datatype of columns except column 'Team' to int64 datatype

In [31]:
# Take a copy of dataframe before making any changes to the value or column name

basketball_df_copy = basketball_df.copy(deep=True)
basketball_df_copy
Out[31]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion Runner-up TeamLaunch HighestPositionHeld
0 Team 1 86 4385 2762 1647 552 563 5947 3140 33 23 1929 1
1 Team 2 86 4262 2762 1581 573 608 5900 3114 25 25 1929 1
2 Team 3 80 3442 2614 1241 598 775 4534 3309 10 8 1929 1
3 Team 4 82 3386 2664 1187 616 861 4398 3469 6 6 1931to32 1
4 Team 5 86 3368 2762 1209 633 920 4631 3700 8 7 1929 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
56 Team 57 1 34 38 8 10 20 38 66 - - 2009-10 20
57 Team 58 1 22 30 7 8 15 37 57 - - 1956-57 16
58 Team 59 1 19 30 7 5 18 51 85 - - 1951~52 16
59 Team 60 1 14 30 5 4 21 34 65 - - 1955-56 15
60 Team 61 1 - - - - - - - - - 2017~18 9

61 rows × 13 columns

In [32]:
# Rename column name as the column name contains special character '-'

basketball_df.rename(columns = {'Runner-up': 'RunnerUp'}, inplace = True)
basketball_df.columns
Out[32]:
Index(['Team', 'Tournament', 'Score', 'PlayedGames', 'WonGames', 'DrawnGames',
       'LostGames', 'BasketScored', 'BasketGiven', 'TournamentChampion',
       'RunnerUp', 'TeamLaunch', 'HighestPositionHeld'],
      dtype='object')
In [33]:
# Take first 4 values from column 'Teamlaunch' and save the sliced value in same column 'TeamLaunch'

basketball_df['TeamLaunch'] = basketball_df['TeamLaunch'].str.slice(0, 4)
basketball_df['TeamLaunch']
Out[33]:
0     1929
1     1929
2     1929
3     1931
4     1929
      ... 
56    2009
57    1956
58    1951
59    1955
60    2017
Name: TeamLaunch, Length: 61, dtype: object
In [34]:
# Replace special characters to 0 in all rows

basketball_df = basketball_df.replace('-', 0)
basketball_df
Out[34]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion RunnerUp TeamLaunch HighestPositionHeld
0 Team 1 86 4385 2762 1647 552 563 5947 3140 33 23 1929 1
1 Team 2 86 4262 2762 1581 573 608 5900 3114 25 25 1929 1
2 Team 3 80 3442 2614 1241 598 775 4534 3309 10 8 1929 1
3 Team 4 82 3386 2664 1187 616 861 4398 3469 6 6 1931 1
4 Team 5 86 3368 2762 1209 633 920 4631 3700 8 7 1929 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
56 Team 57 1 34 38 8 10 20 38 66 0 0 2009 20
57 Team 58 1 22 30 7 8 15 37 57 0 0 1956 16
58 Team 59 1 19 30 7 5 18 51 85 0 0 1951 16
59 Team 60 1 14 30 5 4 21 34 65 0 0 1955 15
60 Team 61 1 0 0 0 0 0 0 0 0 0 2017 9

61 rows × 13 columns

In [35]:
# Remove the space in column value 'Team'

basketball_df['Team'] = basketball_df['Team'].str.replace('Team ', 'Team');
basketball_df['Team']
Out[35]:
0      Team1
1      Team2
2      Team3
3      Team4
4      Team5
       ...  
56    Team57
57    Team58
58    Team59
59    Team60
60    Team61
Name: Team, Length: 61, dtype: object
In [36]:
# Change datatype to int64 for below mentioned columns

columns = ['Tournament', 'Score', 'PlayedGames', 'WonGames', 'DrawnGames',
       'LostGames', 'BasketScored', 'BasketGiven', 'TournamentChampion',
       'RunnerUp', 'HighestPositionHeld']

basketball_df[columns] = basketball_df[columns].apply(pd.to_numeric, errors='coerce')

basketball_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 13 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   Team                 61 non-null     object
 1   Tournament           61 non-null     int64 
 2   Score                61 non-null     int64 
 3   PlayedGames          61 non-null     int64 
 4   WonGames             61 non-null     int64 
 5   DrawnGames           61 non-null     int64 
 6   LostGames            61 non-null     int64 
 7   BasketScored         61 non-null     int64 
 8   BasketGiven          61 non-null     int64 
 9   TournamentChampion   61 non-null     int64 
 10  RunnerUp             61 non-null     int64 
 11  TeamLaunch           61 non-null     object
 12  HighestPositionHeld  61 non-null     int64 
dtypes: int64(11), object(2)
memory usage: 6.3+ KB
In [37]:
# Data cleaning is completed. Data set will look like below.

basketball_df.tail()
Out[37]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion RunnerUp TeamLaunch HighestPositionHeld
56 Team57 1 34 38 8 10 20 38 66 0 0 2009 20
57 Team58 1 22 30 7 8 15 37 57 0 0 1956 16
58 Team59 1 19 30 7 5 18 51 85 0 0 1951 16
59 Team60 1 14 30 5 4 21 34 65 0 0 1955 15
60 Team61 1 0 0 0 0 0 0 0 0 0 2017 9
In [38]:
# Creating new column WonGamesPercentage.

basketball_df['WonGamesPercentage'] = basketball_df['WonGames']/basketball_df['PlayedGames']
basketball_df['WonGamesPercentage']
Out[38]:
0     0.596307
1     0.572411
2     0.474751
3     0.445571
4     0.437726
        ...   
56    0.210526
57    0.233333
58    0.233333
59    0.166667
60         NaN
Name: WonGamesPercentage, Length: 61, dtype: float64
In [39]:
# Creating new column DrawnGamesPercentage.

basketball_df['DrawnGamesPercentage'] = round(basketball_df['DrawnGames'] / basketball_df['PlayedGames'], 2)
basketball_df['DrawnGamesPercentage']
Out[39]:
0     0.20
1     0.21
2     0.23
3     0.23
4     0.23
      ... 
56    0.26
57    0.27
58    0.17
59    0.13
60     NaN
Name: DrawnGamesPercentage, Length: 61, dtype: float64
In [40]:
# Creating new column LostGamesPercentage.

basketball_df['LostGamesPercentage'] = round(basketball_df['LostGames'] / basketball_df['PlayedGames'], 2)
basketball_df['LostGamesPercentage']
Out[40]:
0     0.20
1     0.22
2     0.30
3     0.32
4     0.33
      ... 
56    0.53
57    0.50
58    0.60
59    0.70
60     NaN
Name: LostGamesPercentage, Length: 61, dtype: float64
In [41]:
# Replace the Nan values with zero in the dataframe

basketball_df.replace(np.nan, 0, inplace=True)
In [42]:
basketball_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 16 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Team                  61 non-null     object 
 1   Tournament            61 non-null     int64  
 2   Score                 61 non-null     int64  
 3   PlayedGames           61 non-null     int64  
 4   WonGames              61 non-null     int64  
 5   DrawnGames            61 non-null     int64  
 6   LostGames             61 non-null     int64  
 7   BasketScored          61 non-null     int64  
 8   BasketGiven           61 non-null     int64  
 9   TournamentChampion    61 non-null     int64  
 10  RunnerUp              61 non-null     int64  
 11  TeamLaunch            61 non-null     object 
 12  HighestPositionHeld   61 non-null     int64  
 13  WonGamesPercentage    61 non-null     float64
 14  DrawnGamesPercentage  61 non-null     float64
 15  LostGamesPercentage   61 non-null     float64
dtypes: float64(3), int64(11), object(2)
memory usage: 7.8+ KB
In [43]:
basketball_df.tail()
Out[43]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion RunnerUp TeamLaunch HighestPositionHeld WonGamesPercentage DrawnGamesPercentage LostGamesPercentage
56 Team57 1 34 38 8 10 20 38 66 0 0 2009 20 0.210526 0.26 0.53
57 Team58 1 22 30 7 8 15 37 57 0 0 1956 16 0.233333 0.27 0.50
58 Team59 1 19 30 7 5 18 51 85 0 0 1951 16 0.233333 0.17 0.60
59 Team60 1 14 30 5 4 21 34 65 0 0 1955 15 0.166667 0.13 0.70
60 Team61 1 0 0 0 0 0 0 0 0 0 2017 9 0.000000 0.00 0.00
  1. Perform detailed statistical analysis and EDA using univariate, bi-variate and multivariate EDA techniques to get data driven insights on recommending which teams they can approach which will be a deal win for them. Also as a data and statistics expert you have to develop a detailed performance report using this data. [10 Marks]

Hint: Use statistical techniques and visualisation techniques to come up with useful metrics and reporting. Find out the best performing team, oldest team, team with highest goals, team with lowest performance etc. and many more. These are just random examples. Please use your best analytical approach to build this report. You can mix match columns to create new ones which can be used for better analysis. Create your own features if required. Be highly experimental and analytical here to find hidden patterns. Use graphical interactive libraries to enable you to publish interactive plots in python.

In [44]:
basketball_df.describe(include = "all").T
Out[44]:
count unique top freq mean std min 25% 50% 75% max
Team 61 61 Team1 1 NaN NaN NaN NaN NaN NaN NaN
Tournament 61.0 NaN NaN NaN 24.0 26.827225 1.0 4.0 12.0 38.0 86.0
Score 61.0 NaN NaN NaN 901.42623 1134.899121 0.0 96.0 375.0 1351.0 4385.0
PlayedGames 61.0 NaN NaN NaN 796.819672 876.282765 0.0 114.0 423.0 1318.0 2762.0
WonGames 61.0 NaN NaN NaN 303.967213 406.99103 0.0 34.0 123.0 426.0 1647.0
DrawnGames 61.0 NaN NaN NaN 188.934426 201.799477 0.0 24.0 95.0 330.0 633.0
LostGames 61.0 NaN NaN NaN 303.754098 294.708594 0.0 62.0 197.0 563.0 1070.0
BasketScored 61.0 NaN NaN NaN 1140.344262 1506.740211 0.0 153.0 430.0 1642.0 5947.0
BasketGiven 61.0 NaN NaN NaN 1140.229508 1163.710766 0.0 221.0 632.0 1951.0 3889.0
TournamentChampion 61.0 NaN NaN NaN 1.42623 5.472535 0.0 0.0 0.0 0.0 33.0
RunnerUp 61.0 NaN NaN NaN 1.409836 4.540107 0.0 0.0 0.0 0.0 25.0
TeamLaunch 61 45 1929 10 NaN NaN NaN NaN NaN NaN NaN
HighestPositionHeld 61.0 NaN NaN NaN 7.081967 5.276663 1.0 3.0 6.0 10.0 20.0
WonGamesPercentage 61.0 NaN NaN NaN 0.308506 0.087426 0.0 0.27193 0.302817 0.334854 0.596307
DrawnGamesPercentage 61.0 NaN NaN NaN 0.232623 0.054034 0.0 0.22 0.24 0.26 0.39
LostGamesPercentage 61.0 NaN NaN NaN 0.442623 0.101422 0.0 0.41 0.46 0.49 0.7
In [45]:
basketball_df.skew()

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_8420\1534467484.py:1: FutureWarning: The default value of numeric_only in DataFrame.skew is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.
  basketball_df.skew()
Out[45]:
Tournament              1.217038
Score                   1.593109
PlayedGames             1.141978
WonGames                1.805728
DrawnGames              1.004159
LostGames               0.897130
BasketScored            1.777436
BasketGiven             0.975859
TournamentChampion      4.777021
RunnerUp                4.360643
TeamLaunch              0.672956
HighestPositionHeld     0.817976
WonGamesPercentage      0.404140
DrawnGamesPercentage   -1.434088
LostGamesPercentage    -1.448581
dtype: float64
In [46]:
# Univariate analysis Numerical columns - Distribution Plots

rows = 3
cols = 5
iterator = 1

plt.figure(figsize=(10, 5))
columns = basketball_df.select_dtypes(include = np.number).columns

for i, column in enumerate(basketball_df[columns], iterator):
    plt.subplot(rows, cols, i)
    sns.histplot(basketball_df[column], kde = True)
    plt.title(column)
    plt.xticks(rotation = 90)
plt.tight_layout()
plt.show()

Observations :

1. 'Tournament', 'Score', 'PlayedGames', 'WonGames', 'DrawnGames',
   'LostGames', 'BasketScored', 'BasketGiven', 'TournamentChampion',
   'RunnerUp', 'TeamLaunch', 'HighestPositionHeld' are Right skewed.

2. 'WonGames', 'DrawnGames', 'LostGames' are left skewed.
In [47]:
# Univariate analysis Numerical columns - Box Plots

rows = 3
cols = 5
iterator = 1

plt.figure(figsize=(10, 5))
columns = basketball_df.select_dtypes(include = np.number).columns

for i, column in enumerate(basketball_df[columns], iterator):
    plt.subplot(rows, cols, i)
    sns.boxplot(basketball_df[column])
    plt.title(column)
    plt.xticks(rotation = 90)
plt.tight_layout()
plt.show()

Observations :

1. 'Tournament', 'Score', 'PlayedGames', 'WonGames', 'DrawnGames',
   'LostGames', 'BasketScored', 'BasketGiven', 'TournamentChampion',
   'RunnerUp', 'TeamLaunch', 'HighestPositionHeld' are Right skewed.

2. 'WonGames', 'DrawnGames', 'LostGames' are left skewed.
In [48]:
sns.lineplot(x = 'Team', y = 'PlayedGames', data=basketball_df.head(25), color='blue')
sns.lineplot(x = 'Team', y = 'WonGames', data=basketball_df.head(25), color='red')
sns.lineplot(x = 'Team', y = 'DrawnGames', data=basketball_df.head(25), color='green')
sns.lineplot(x = 'Team', y = 'LostGames', data=basketball_df.head(25), color='orange')
sns.lineplot(x = 'Team', y = 'BasketScored', data=basketball_df.head(25), color='black')
sns.lineplot(x = 'Team', y = 'BasketGiven', data=basketball_df.head(25), color='purple')
sns.lineplot(x = 'Team', y = 'RunnerUp', data=basketball_df.head(25), color='pink')
sns.lineplot(x = 'Team', y = 'HighestPositionHeld', data=basketball_df.head(25), color='grey')
sns.lineplot(x = 'Team', y = 'WonGamesPercentage', data=basketball_df.head(25), color='chocolate')
sns.lineplot(x = 'Team', y = 'DrawnGamesPercentage', data=basketball_df.head(25), color='yellow')
sns.lineplot(x = 'Team', y = 'LostGamesPercentage', data=basketball_df.head(25), color='cyan')
sns.lineplot(x = 'Team', y = 'Score', data=basketball_df.head(25), color='brown')
plt.grid()
plt.xticks(rotation=90)
plt.legend(labels=['PlayedGames', 'WonGames', 'DrawnGames', 'LostGames','BasketScored','BasketGiven','RunnerUp','HighestPositionHeld','WonGamesPercentage','DrawnGamesPercentage','LostGamesPercentage','Score'])
plt.show()
In [49]:
# Univariate analysis Categorical columns - Count/Bar Plots

rows = 2
cols = 1
iterator = 1

plt.figure(figsize=(10,5))
columns = basketball_df.select_dtypes(include = 'object').columns

for i, column in enumerate(basketball_df[columns], iterator):
    plt.subplot(rows, cols, i)
    sns.countplot(data = basketball_df, x = column)
    plt.title(column)
    plt.xticks(rotation = 90)
plt.tight_layout()
plt.show()

Observations :

1. There are total 61 teams.
2. In 1929, there are total of 10 teams were launched.
3. In 1941, there are total of 3 teams were launched.
4. In each year minimum of 1 team launched the team.
In [50]:
# Bivariate analysis Numerical columns vs Categorical columns (Team) - Bar Plots

px.bar(data_frame=basketball_df, x='Team', y='Tournament', color='Team')

Observations :

1. There are total 61 teams.
2. Team1, Team2, Team5 won 86 Tournment.
3. Tournament data looks right skewed.
In [51]:
px.bar(data_frame=basketball_df, x='Team', y='Score', color='Team')

Observations :

1. There are total 61 teams.
2. Team1 scored 4385 highest, Team2 scored 4262 as second highest score.
3. Score data looks right skewed.
In [52]:
px.bar(data_frame=basketball_df, x='Team', y='PlayedGames', color='Team')

Observations :

1. There are total 61 teams.
2. Team1, Team2, Team3 played 2762 games.
3. PlayedGames data looks right skewed.
In [53]:
px.bar(data_frame=basketball_df, x='Team', y='WonGames', color='Team')

Observations :

1. There are total 61 teams.
2. Team1 won 1647 highest, Team2 won 1581 as second highest.
3. WonGames data looks right skewed.
In [54]:
px.bar(data_frame=basketball_df, x='Team', y='DrawnGames', color='Team')

Observations :

1. There are total 61 teams.
2. Team5 drawn 633 highest, Team4 drawn 612 as second highest.
3. DrawnGames data looks right skewed.
In [55]:
px.bar(data_frame=basketball_df, x='Team', y='LostGames', color='Team')

Observations :

1. There are total 61 teams.
2. Team7 lost 1070 highest, Team5 lost 920 as second highest.
3. LostGames data looks right skewed.
In [56]:
px.bar(data_frame=basketball_df, x='Team', y='BasketScored', color='Team')

Observations :

1. There are total 61 teams.
2. Team1 scored 5947 highest, Team2 scored 5900 as second highest.
3. BasketScored data looks right skewed.
In [57]:
px.bar(data_frame=basketball_df, x='Team', y='BasketGiven', color='Team')

Observations :

1. There are total 61 teams.
2. Team7 given 3889 highest, Team5 given 3700 as second highest.
3. BasketGiven data looks right skewed.
In [58]:
px.bar(data_frame=basketball_df, x='Team', y='TournamentChampion', color='Team')

Observations :

1. There are total 61 teams.
2. Team1 tournamentchampion 33 highest, Team2 tournamentchampion 25 as second highest.
3. TournamentChampion data looks right skewed.
In [59]:
px.bar(data_frame=basketball_df, x='Team', y='RunnerUp', color='Team')

Observations :

1. There are total 61 teams.
2. Team2 runnerup 25 highest, Team1 runnerup 23 as second highest.
3. RunnerUp data looks right skewed.
In [60]:
px.bar(data_frame=basketball_df, x='Team', y='HighestPositionHeld', color='Team')

Observations :

1. There are total 61 teams.
2. Team57 highestpositionheld 33 highest, Team49 highestpositionheld 25 as second highest.
3. HighestPositionHeld data looks left skewed.
In [61]:
px.bar(data_frame=basketball_df, x='Team', y='WonGamesPercentage', color='Team')

Observations :

1. There are total 61 teams.
2. Team1 won games %age 0.59% highest, Team2 won games %age 0.57% as second highest.
3. WonGamesPercentage data looks right skewed.
In [62]:
px.bar(data_frame=basketball_df, x='Team', y='DrawnGamesPercentage', color='Team')

Observations :

1. There are total 61 teams.
2. Team46 drawn games %age 0.39% highest, Team49 won games %age 0.30% as second highest.
3. DrawnGamesPercentage data looks left skewed.
In [63]:
px.bar(data_frame=basketball_df, x='Team', y='LostGamesPercentage', color='Team')

Observations :

1. There are total 61 teams.
2. Team60 lost games %age 0.70% highest, Team55 and Team59 lost games %age 0.60% as second highest.
3. LostGamesPercentage data looks left skewed.
In [64]:
basketball_df.sort_values(by = ['WonGamesPercentage'], ascending=False).head(10)
Out[64]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion RunnerUp TeamLaunch HighestPositionHeld WonGamesPercentage DrawnGamesPercentage LostGamesPercentage
0 Team1 86 4385 2762 1647 552 563 5947 3140 33 23 1929 1 0.596307 0.20 0.20
1 Team2 86 4262 2762 1581 573 608 5900 3114 25 25 1929 1 0.572411 0.21 0.22
2 Team3 80 3442 2614 1241 598 775 4534 3309 10 8 1929 1 0.474751 0.23 0.30
3 Team4 82 3386 2664 1187 616 861 4398 3469 6 6 1931 1 0.445571 0.23 0.32
4 Team5 86 3368 2762 1209 633 920 4631 3700 8 7 1929 1 0.437726 0.23 0.33
20 Team21 17 970 646 266 172 208 892 789 0 1 1998 2 0.411765 0.27 0.32
5 Team6 73 2819 2408 990 531 887 3680 3373 1 4 1934 1 0.411130 0.22 0.37
7 Team8 70 2573 2302 864 577 861 3228 3230 2 3 1929 1 0.375326 0.25 0.37
10 Team11 45 1814 1530 563 392 575 2052 2188 1 5 1941 1 0.367974 0.26 0.38
6 Team7 82 2792 2626 948 608 1070 3609 3889 0 0 1929 3 0.361005 0.23 0.41

Observations :

1. There are total 61 teams.
2. Team1 won games %age 0.59% highest, Team2 won games %age 0.57% as second highest.
3. WonGamesPercentage data looks right skewed.
In [65]:
basketball_df.sort_values(by = ['DrawnGamesPercentage'], ascending=False).head(10)
Out[65]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion RunnerUp TeamLaunch HighestPositionHeld WonGamesPercentage DrawnGamesPercentage LostGamesPercentage
45 Team46 3 96 114 26 44 44 101 139 0 0 1990 9 0.228070 0.39 0.39
48 Team49 2 81 80 19 24 37 70 115 0 0 1995 19 0.237500 0.30 0.46
55 Team56 1 35 38 8 11 19 36 55 0 0 2016 17 0.210526 0.29 0.50
47 Team48 2 83 80 20 23 37 62 117 0 0 1996 17 0.250000 0.29 0.46
32 Team33 12 343 448 104 127 217 393 662 0 0 1977 12 0.232143 0.28 0.48
35 Team36 7 277 270 76 76 118 320 410 0 0 1991 7 0.281481 0.28 0.44
38 Team39 4 190 160 52 45 63 199 241 0 0 1994 10 0.325000 0.28 0.39
33 Team34 9 293 346 96 92 158 291 489 0 0 1987 7 0.277457 0.27 0.46
23 Team24 21 606 678 203 180 295 750 1022 0 0 1959 5 0.299410 0.27 0.44
57 Team58 1 22 30 7 8 15 37 57 0 0 1956 16 0.233333 0.27 0.50

Observations :

1. There are total 61 teams.
2. Team46 drawn games %age 0.39% highest, Team49 won games %age 0.30% as second highest.
3. DrawnGamesPercentage data looks left skewed.
In [66]:
basketball_df.sort_values(by = ['LostGamesPercentage'], ascending=False).head(10)
Out[66]:
Team Tournament Score PlayedGames WonGames DrawnGames LostGames BasketScored BasketGiven TournamentChampion RunnerUp TeamLaunch HighestPositionHeld WonGamesPercentage DrawnGamesPercentage LostGamesPercentage
59 Team60 1 14 30 5 4 21 34 65 0 0 1955 15 0.166667 0.13 0.70
54 Team55 2 40 68 13 14 41 70 182 0 0 1950 16 0.191176 0.21 0.60
58 Team59 1 19 30 7 5 18 51 85 0 0 1951 16 0.233333 0.17 0.60
49 Team50 4 76 108 30 16 62 145 252 0 0 1945 10 0.277778 0.15 0.57
46 Team47 4 91 116 34 16 66 181 295 0 0 1947 7 0.293103 0.14 0.57
53 Team54 3 42 54 18 6 30 97 131 0 0 1929 8 0.333333 0.11 0.56
56 Team57 1 34 38 8 10 20 38 66 0 0 2009 20 0.210526 0.26 0.53
50 Team51 3 71 90 29 13 48 121 183 0 0 1953 14 0.322222 0.14 0.53
51 Team52 4 56 72 21 14 37 153 184 0 0 1929 6 0.291667 0.19 0.51
27 Team28 18 445 586 145 143 298 607 992 0 0 1940 11 0.247440 0.24 0.51

Observations :

1. There are total 61 teams.
2. Team60 lost games %age 0.70% highest, Team55 and Team59 lost games %age 0.60% as second highest.
3. LostGamesPercentage data looks left skewed.
In [67]:
# Bivariate analysis Categorical columns vs Categorical columns - Crosstab Plots

pd.crosstab(basketball_df['Team'], basketball_df['TeamLaunch'])
Out[67]:
TeamLaunch 1929 1930 1931 1932 1933 1934 1935 1939 1940 1941 ... 1995 1996 1998 1999 2004 2007 2009 2014 2016 2017
Team
Team1 1 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Team10 0 0 0 1 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Team11 0 0 0 0 0 0 0 0 0 1 ... 0 0 0 0 0 0 0 0 0 0
Team12 0 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 0 0
Team13 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Team60 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Team61 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 1
Team7 1 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Team8 1 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Team9 0 0 0 0 0 0 0 1 0 0 ... 0 0 0 0 0 0 0 0 0 0

61 rows × 45 columns

Observations :

1. Bivariate anaylsis plots the important categorical metrics.
2. Plotting 2 categorical variables, Team and TeamLaunch.
In [68]:
# Multivariate analysis Numerical columns - Heatmap Plots

sns.heatmap(basketball_df.corr(), annot=True, cmap='autumn')
plt.show()

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_8420\1483333238.py:3: FutureWarning:

The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  1. Please include any improvements or suggestions to the association management on quality, quantity, variety, velocity, veracity etc. on the data points collected by the association to perform a better data analysis in future. At-least 1 suggestion for each point. [10 Marks]

Quality: For some columns like Team61, we have incomplete information where none of the records available except TeamLaunch and HighestPositionHeld columns. As this information is not enough to apply any strategies.

Quantity: Since our dataset had only 61 entries, we couldn't figure out more trends. We can include information about the players of each team, at least of the captains. It would have given us some information about the team's performance under different captains.

Variety: To analyze a bastball team other then the given information , more subject related data can be added . Example: HighestPositionHeld column . Dont have target value to analyze team.

Velocity: efficient speed of data collection can be achieved if we maintain a regular record of the games, keep adding data after every match.

Veracity: Our data set is small which contains onlt 61 teams, it has out liers and missing information. With these, Eventually it effects the reliability of its results.

Data set has outdated information check as we ahev few old teams information like Team58, Team59, Team60 which team launched on 1950's and played less games.

Teamwise data prediction may vary because it will be based on players. So better we gather more information about teams and its players.

We can add more identifiers to the data about team. More the identifiers will help us to analyze teams accurately.

Part C - 15 Marks¶

• DOMAIN: Startup ecosystem

• CONTEXT: Company X is a EU online publisher focusing on the startups industry. The company specifically reports on the business related to technology news, analysis of emerging trends and profiling of new tech businesses and products. Their event i.e. Startup Battlefield is the world’s pre-eminent startup competition. Startup Battlefield features 15-30 top early stage startups pitching top judges in front of a vast live audience, present in person and online.

• DATA DESCRIPTION: CompanyX_EU.csv - Each row in the dataset is a Start-up company and the columns describe the company.

• DATA DICTIONARY:

1. Startup: Name of the company
2. Product: Actual product
3. Funding: Funds raised by the company in USD
4. Event: The event the company participated in
5. Result: Described by Contestant, Finalist, Audience choice, Winner or Runner up
6. OperatingState: Current status of the company, Operating ,Closed, Acquired or IPO

*Dataset has been downloaded from the internet. All the credit for the dataset goes to the original creator of the data.

• PROJECT OBJECTIVE: Analyse the data of the various companies from the given dataset and perform the tasks that are specified in the below steps. Draw insights from the various attributes that are present in the dataset, plot distributions, state hypotheses and draw conclusions from the dataset.

• STEPS AND TASK [15 Marks]:

  1. Read the CSV file.
In [69]:
# Read the data set

company_df = pd.read_csv('CompanyX_EU.csv');
company_df
Out[69]:
Startup Product Funding Event Result OperatingState
0 2600Hz 2600hz.com NaN Disrupt SF 2013 Contestant Operating
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed
2 3DPrinterOS 3dprinteros.com NaN Disrupt SF 2016 Contestant Operating
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating
4 42 Technologies 42technologies.com NaN Disrupt NYC 2013 Contestant Operating
... ... ... ... ... ... ...
657 Zivity zivity.com $8M TC40 2007 Contestant Operating
658 Zmorph zmorph3d.com $1M - Audience choice Operating
659 Zocdoc zocdoc.com $223M TC40 2007 Contestant Operating
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating

662 rows × 6 columns

  1. Data Exploration: [1 Mark]

A. Check the datatypes of each attribute.

In [70]:
company_df.dtypes
Out[70]:
Startup           object
Product           object
Funding           object
Event             object
Result            object
OperatingState    object
dtype: object

B. Check for null values in the attributes.

In [71]:
company_df.isnull()
Out[71]:
Startup Product Funding Event Result OperatingState
0 False False True False False False
1 False False False False False False
2 False False True False False False
3 False False False False False False
4 False False True False False False
... ... ... ... ... ... ...
657 False False False False False False
658 False False False False False False
659 False False False False False False
660 False False False False False False
661 False False False False False False

662 rows × 6 columns

In [ ]:
In [72]:
company_df.isna().sum()
Out[72]:
Startup             0
Product             6
Funding           214
Event               0
Result              0
OperatingState      0
dtype: int64
  1. Data preprocessing & visualisation: [4 Marks]

A. Drop the null values. [1 Mark]

In [73]:
company_df.dropna(inplace = True)
company_df
Out[73]:
Startup Product Funding Event Result OperatingState
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating
5 5to1 5to1.com $19.3M TC50 2009 Contestant Acquired
6 8 Securities 8securities.com $29M Disrupt Beijing 2011 Finalist Operating
10 AdhereTech adheretech.com $1.8M Hardware Battlefield 2014 Contestant Operating
... ... ... ... ... ... ...
657 Zivity zivity.com $8M TC40 2007 Contestant Operating
658 Zmorph zmorph3d.com $1M - Audience choice Operating
659 Zocdoc zocdoc.com $223M TC40 2007 Contestant Operating
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating

446 rows × 6 columns

Observations :

  1. Before dropping the null values, company_df record was 662 rows × 6 columns and post dropping the null values, company_df record is 446 rows × 6 columns

B. Convert the ‘Funding’ features to a numerical value.

(Execute below code)

df1.loc[:,'Funds_in_million'] = df1['Funding'].apply(lambda x: float(x[1:-1])/1000 if x[-1] == 'K' else (float(x[1:-1])*1000 if x[-1] == 'B' else float(x[1:-1])))

In [74]:
company_df.loc[:,'Funds_in_million'] = company_df['Funding'].apply(lambda x: float(x[1:-1])/1000 if x[-1] == 'K' else (float(x[1:-1])*1000 if x[-1] == 'B' else float(x[1:-1])))
company_df
Out[74]:
Startup Product Funding Event Result OperatingState Funds_in_million
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed 0.63
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating 1.00
5 5to1 5to1.com $19.3M TC50 2009 Contestant Acquired 19.30
6 8 Securities 8securities.com $29M Disrupt Beijing 2011 Finalist Operating 29.00
10 AdhereTech adheretech.com $1.8M Hardware Battlefield 2014 Contestant Operating 1.80
... ... ... ... ... ... ... ...
657 Zivity zivity.com $8M TC40 2007 Contestant Operating 8.00
658 Zmorph zmorph3d.com $1M - Audience choice Operating 1.00
659 Zocdoc zocdoc.com $223M TC40 2007 Contestant Operating 223.00
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating 3.40
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating 31.50

446 rows × 7 columns

C. Plot box plot for funds in million. [1 Mark]

In [75]:
box_plot = sns.boxplot(x = company_df['Funds_in_million'])
box_plot
Out[75]:
<Axes: xlabel='Funds_in_million'>

D. Check the number of outliers greater than the upper fence. [1 Mark]

In [76]:
def find_outliers_IQR(company_df):
    q1 = company_df.quantile(0.25)
    q3 = company_df.quantile(0.75)
    
    IQR = q3-q1
    
    outliers = company_df[((company_df<(q1-1.5*IQR)) | (company_df>(q3+1.5*IQR)))]
    return outliers
In [ ]:
In [77]:
outliers = find_outliers_IQR(company_df["Funds_in_million"])

print('Number of outliers: ' + str(len(outliers)))
print('Max outlier value: ' + str(outliers.max()))
print('Min outlier value: ' + str(outliers.min()))

outliers

Number of outliers: 60
Max outlier value: 1700.0
Min outlier value: 24.0
Out[77]:
6        29.0
31       24.0
40       50.9
49       40.0
56      205.0
108      32.5
113     182.1
128      35.4
130      38.0
132      37.1
138      72.0
139      64.0
154    1700.0
166      34.6
172      35.5
179      30.1
180      26.0
188      66.0
191      28.3
209     103.0
213      25.9
215      36.5
225      44.7
231      24.2
271     122.4
276      34.9
279      67.8
282     166.1
302      42.1
305      69.0
313      65.1
325      25.1
346      31.8
389      37.0
393      38.5
394      25.5
398      24.0
427     278.0
432      24.0
435      63.0
460      25.0
471     160.0
546      40.0
555      47.4
560      24.5
581      25.0
593     332.4
598      35.0
606     168.8
615      28.0
625      30.8
643      41.8
644     142.0
647     117.8
649      26.0
650      28.0
654      62.1
656     583.6
659     223.0
661      31.5
Name: Funds_in_million, dtype: float64
In [ ]:
In [78]:
# To cap the outliers, calculate a upper limit and lower limit. For the upper limit, we will use the mean plus three 
# standard deviations. For the lower limit, we will calculate it as the mean minus 3 standard deviations. Keep in mind, 
# the calculation you use can depend on the data’s distribution.

upper_limit = company_df['Funds_in_million'].mean() + 3*company_df['Funds_in_million'].std()
print('upper_limit: ' + str(upper_limit))

lower_limit = company_df['Funds_in_million'].mean() - 3*company_df['Funds_in_million'].std()
print('lower_limit: ' + str(lower_limit))

upper_limit: 288.69261876000576
lower_limit: -254.20964162996088
In [ ]:
In [79]:
company_df.describe().T
Out[79]:
count mean std min 25% 50% 75% max
Funds_in_million 446.0 17.241489 90.48371 0.005 0.74525 2.2 9.475 1700.0
In [ ]:
In [80]:
# After calculating the upper and lower limit, we use the numpy .where() function to apply the limits to 'Funds_in_million'.

# company_df['Funds_in_million'] = np.where(company_df['Funds_in_million'] > upper_limit,
#    upper_limit,
#    np.where(company_df['Funds_in_million'] < lower_limit,
#        lower_limit,
#        company_df['Funds_in_million']
#    )
# )
In [ ]:
In [81]:
company_df.describe().T
Out[81]:
count mean std min 25% 50% 75% max
Funds_in_million 446.0 17.241489 90.48371 0.005 0.74525 2.2 9.475 1700.0

E. Check frequency of the OperatingState features classes. [1 Mark]

In [82]:
company_df['OperatingState'].value_counts()
Out[82]:
Operating    319
Acquired      66
Closed        57
Ipo            4
Name: OperatingState, dtype: int64

Observations:

  1. There are 319 Operating companies, 66 Acquired companies, 57 Closed companies and 4 Ipo companies.
  1. Statistical Analysis: [10 Marks]

A. Is there any significant difference between Funds raised by companies that are still operating vs companies that closed down? [1 Mark]

In [83]:
company_df.groupby('OperatingState').sum()

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_8420\707961184.py:1: FutureWarning:

The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
Out[83]:
Funds_in_million
OperatingState
Acquired 872.0510
Closed 185.7157
Ipo 551.1000
Operating 6080.8372

Observations:

  1. Yes there is a significant difference between Funds raised by companies that are still operating vs companies that closed down.
  2. The Operating companies have raised 6080.8372 millions and companies that have been closed raised just 185.7157 millions.

B. Write the null hypothesis and alternative hypothesis. [1 Mark]

In [84]:
# Create new data set with null which fills 0 with Nan

company_df_with_null = pd.read_csv('CompanyX_EU.csv')
company_df_with_null = company_df_with_null.fillna(0)
company_df_with_null
Out[84]:
Startup Product Funding Event Result OperatingState
0 2600Hz 2600hz.com 0 Disrupt SF 2013 Contestant Operating
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed
2 3DPrinterOS 3dprinteros.com 0 Disrupt SF 2016 Contestant Operating
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating
4 42 Technologies 42technologies.com 0 Disrupt NYC 2013 Contestant Operating
... ... ... ... ... ... ...
657 Zivity zivity.com $8M TC40 2007 Contestant Operating
658 Zmorph zmorph3d.com $1M - Audience choice Operating
659 Zocdoc zocdoc.com $223M TC40 2007 Contestant Operating
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating

662 rows × 6 columns

In [ ]:
In [85]:
# Create new data set without null by dropping Nan

company_df_without_null = pd.read_csv('CompanyX_EU.csv')
company_df_without_null = company_df_without_null.dropna()
company_df_without_null
Out[85]:
Startup Product Funding Event Result OperatingState
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating
5 5to1 5to1.com $19.3M TC50 2009 Contestant Acquired
6 8 Securities 8securities.com $29M Disrupt Beijing 2011 Finalist Operating
10 AdhereTech adheretech.com $1.8M Hardware Battlefield 2014 Contestant Operating
... ... ... ... ... ... ...
657 Zivity zivity.com $8M TC40 2007 Contestant Operating
658 Zmorph zmorph3d.com $1M - Audience choice Operating
659 Zocdoc zocdoc.com $223M TC40 2007 Contestant Operating
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating

446 rows × 6 columns

In [ ]:
In [86]:
company_df
Out[86]:
Startup Product Funding Event Result OperatingState Funds_in_million
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed 0.63
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating 1.00
5 5to1 5to1.com $19.3M TC50 2009 Contestant Acquired 19.30
6 8 Securities 8securities.com $29M Disrupt Beijing 2011 Finalist Operating 29.00
10 AdhereTech adheretech.com $1.8M Hardware Battlefield 2014 Contestant Operating 1.80
... ... ... ... ... ... ... ...
657 Zivity zivity.com $8M TC40 2007 Contestant Operating 8.00
658 Zmorph zmorph3d.com $1M - Audience choice Operating 1.00
659 Zocdoc zocdoc.com $223M TC40 2007 Contestant Operating 223.00
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating 3.40
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating 31.50

446 rows × 7 columns

In [ ]:
In [87]:
company_df_with_null.nunique()
Out[87]:
Startup           662
Product           657
Funding           241
Event              26
Result              5
OperatingState      4
dtype: int64
In [ ]:
In [88]:
company_df_without_null.nunique()
Out[88]:
Startup           446
Product           446
Funding           239
Event              26
Result              5
OperatingState      4
dtype: int64
In [ ]:
In [89]:
company_df.nunique()
Out[89]:
Startup             446
Product             446
Funding             239
Event                26
Result                5
OperatingState        4
Funds_in_million    239
dtype: int64
In [ ]:

With null which are filled to 0 for Nan, independent column 'Funding' is 241. But without null where Nan are dropped, in this scenario independent column 'Funding' is 239.

So, when independent column 'Funding' Nan values are droped, which affects dependent column 'Funds_in_million' also affects that count is 239 same as column 'Funding'.

Hence there is no NULL hypothesis since since variables of a dependent attribute can be affected.

C. Test for significance and conclusion [1 Mark]

1 - There is a significant difference in Funds raised between companies that are still opened and companies that are being closed down.(i.e): Operating companies have raised 6080.8372 millions and companies that have been closed raised just 185.7157 millions.

2 - Columns with Null values can affect the Dependent variable. Which means if we drop the columns that have Null value, the total count (frequence) of millions in 'Funds_in_million' will be decreased from 241 to 239.

D. Make a copy of the original data frame. [1 Mark]

In [90]:
company_df_copy = pd.read_csv('CompanyX_EU.csv')
company_df_copy
Out[90]:
Startup Product Funding Event Result OperatingState
0 2600Hz 2600hz.com NaN Disrupt SF 2013 Contestant Operating
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed
2 3DPrinterOS 3dprinteros.com NaN Disrupt SF 2016 Contestant Operating
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating
4 42 Technologies 42technologies.com NaN Disrupt NYC 2013 Contestant Operating
... ... ... ... ... ... ...
657 Zivity zivity.com $8M TC40 2007 Contestant Operating
658 Zmorph zmorph3d.com $1M - Audience choice Operating
659 Zocdoc zocdoc.com $223M TC40 2007 Contestant Operating
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating

662 rows × 6 columns

E. Check frequency distribution of Result variables. [1 Mark]

In [91]:
plt.hist(company_df['Result'])
#plt.xlabel('Frequency distribution of Result')
Out[91]:
(array([312.,   0.,  25.,   0.,   0.,  65.,   0.,  25.,   0.,  19.]),
 array([0. , 0.4, 0.8, 1.2, 1.6, 2. , 2.4, 2.8, 3.2, 3.6, 4. ]),
 <BarContainer object of 10 artists>)

F. Calculate percentage of winners that are still operating and percentage of contestants that are still operating [1 Mark]

In [92]:
describe_result = company_df.groupby(['Result','OperatingState']).describe()
describe_result
Out[92]:
Funds_in_million
count mean std min 25% 50% 75% max
Result OperatingState
Audience choice Closed 2.0 1.595000 1.986970 0.1900 0.89250 1.5950 2.2975 3.0
Operating 23.0 21.538013 48.480902 0.0200 1.00000 2.1000 4.7000 168.8
Contestant Acquired 45.0 9.752911 15.903357 0.0200 1.00000 3.0000 12.0000 69.0
Closed 50.0 3.140514 5.765642 0.0930 0.38125 0.9085 3.5000 35.5
Ipo 3.0 161.700000 153.531658 34.9000 76.35000 117.8000 225.1000 332.4
Operating 214.0 17.245804 119.197322 0.0050 0.59250 1.8500 5.7000 1700.0
Finalist Acquired 13.0 15.111538 14.897681 0.1500 6.30000 10.3000 17.4000 44.7
Closed 5.0 5.100000 8.785784 0.7000 1.00000 1.2000 1.8000 20.8
Ipo 1.0 66.000000 NaN 66.0000 66.00000 66.0000 66.0000 66.0
Operating 46.0 25.997226 89.806214 0.0500 1.10000 4.6500 16.0750 583.6
Runner up Acquired 1.0 16.500000 NaN 16.5000 16.50000 16.5000 16.5000 16.5
Operating 18.0 19.288150 41.643980 0.0857 1.52500 7.2500 21.1250 182.1
Winner Acquired 7.0 31.460000 49.726584 0.5200 8.70000 10.5000 24.9000 142.0
Operating 18.0 19.544544 25.365468 0.0550 1.70000 11.5500 32.9500 103.0
In [ ]:
In [93]:
# list(company_df.Result.value_counts())

total_count = company_df.Result.value_counts()
total_count
Out[93]:
Contestant         312
Finalist            65
Audience choice     25
Winner              25
Runner up           19
Name: Result, dtype: int64
In [ ]:
In [94]:
total_count_sum = total_count.sum()
total_count_sum
Out[94]:
446
In [ ]:
In [95]:
winner_operating_count = describe_result.iloc[13, 0]
winner_operating_count
Out[95]:
18.0
In [ ]:
In [96]:
# Winner and Operating count is 18

winner_operating_count_percenatge = (winner_operating_count / total_count_sum) * 100
winner_operating_count_percenatge
Out[96]:
4.0358744394618835
In [ ]:
In [97]:
contestant_operating_count = describe_result.iloc[5, 0]
contestant_operating_count
Out[97]:
214.0
In [ ]:
In [98]:
# Contestant and Operating count is 214

contestant_operating_count_percenatge = (contestant_operating_count / total_count_sum) * 100
contestant_operating_count_percenatge
Out[98]:
47.98206278026906
In [ ]:
In [99]:
print('The percentage of winners that are still operating is %1.4f' % winner_operating_count_percenatge, '%')
print('The percentage of contestants that are still operating is %1.4f' % contestant_operating_count_percenatge, '%')

The percentage of winners that are still operating is 4.0359 %
The percentage of contestants that are still operating is 47.9821 %

G. Write your hypothesis comparing the proportion of companies that are operating between winners and contestants: [2 Mark]

The winners that are still operating count is 4% and Constestants that are still operating count is 48%.

So, the companies that are operating have winners are lesser than the companies that have contestants.

H. Test for significance and conclusion [1 Mark]

Since there is lot of significance difference between winners and contestants that are still operating as the percentage of winners is 4% and contestants are 48%.

I. Select only the Event that has ‘disrupt’ keyword from 2013 onwards. [1 Mark]

In [100]:
disrupt_result = company_df[company_df['Event'].str.contains('Disrupt', case=False)]
disrupt_result
Out[100]:
Startup Product Funding Event Result OperatingState Funds_in_million
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed 0.63
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating 1.00
6 8 Securities 8securities.com $29M Disrupt Beijing 2011 Finalist Operating 29.00
13 Agrilyst agrilyst.com $1M Disrupt SF 2015 Winner Operating 1.00
14 Aiden aiden.ai $750K Disrupt London 2016 Contestant Operating 0.75
... ... ... ... ... ... ... ...
650 YourMechanic yourmechanic.com $28M Disrupt SF 2012 Winner Operating 28.00
654 ZEFR zefr.com $62.1M Disrupt NYC 2010 Contestant Operating 62.10
656 Zenefits zenefits.com $583.6M Disrupt NYC 2013 Finalist Operating 583.60
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating 3.40
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating 31.50

318 rows × 7 columns

In [ ]:
In [101]:
# Creating new column 'Disrupt_year' which holds the year

disrupt_result.loc[:,'Disrupt_year'] = disrupt_result['Event'].apply(lambda x: float(x[-4:]) if x[-1] == 'K' else (float(x[-4:]) if x[-1] == 'B' else float(x[-4:])))
disrupt_result

C:\Users\Bhavya Govindrao\AppData\Local\Temp\ipykernel_8420\1084268816.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[101]:
Startup Product Funding Event Result OperatingState Funds_in_million Disrupt_year
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed 0.63 2013.0
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating 1.00 2016.0
6 8 Securities 8securities.com $29M Disrupt Beijing 2011 Finalist Operating 29.00 2011.0
13 Agrilyst agrilyst.com $1M Disrupt SF 2015 Winner Operating 1.00 2015.0
14 Aiden aiden.ai $750K Disrupt London 2016 Contestant Operating 0.75 2016.0
... ... ... ... ... ... ... ... ...
650 YourMechanic yourmechanic.com $28M Disrupt SF 2012 Winner Operating 28.00 2012.0
654 ZEFR zefr.com $62.1M Disrupt NYC 2010 Contestant Operating 62.10 2010.0
656 Zenefits zenefits.com $583.6M Disrupt NYC 2013 Finalist Operating 583.60 2013.0
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating 3.40 2013.0
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating 31.50 2012.0

318 rows × 8 columns

In [ ]:
In [102]:
# Filter and display Disrupt year less then 2013

disrupt_result[disrupt_result['Disrupt_year'] < 2013]
Out[102]:
Startup Product Funding Event Result OperatingState Funds_in_million Disrupt_year
6 8 Securities 8securities.com $29M Disrupt Beijing 2011 Finalist Operating 29.0 2011.0
22 Alicanto alicanto.com $4M Disrupt SF 2012 Contestant Operating 4.0 2012.0
27 Amen. getamen.com $3M Disrupt SF 2011 Contestant Acquired 3.0 2011.0
34 Appbistro appbistro.com $600K Disrupt NYC 2010 Contestant Closed 0.6 2010.0
35 AppChina appchina.com $7.1M Disrupt Beijing 2011 Contestant Operating 7.1 2011.0
... ... ... ... ... ... ... ... ...
632 WeDemand wedemand.com $1M Disrupt SF 2012 Contestant Operating 1.0 2012.0
649 YouNow younow.com $26M Disrupt SF 2011 Contestant Operating 26.0 2011.0
650 YourMechanic yourmechanic.com $28M Disrupt SF 2012 Winner Operating 28.0 2012.0
654 ZEFR zefr.com $62.1M Disrupt NYC 2010 Contestant Operating 62.1 2010.0
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating 31.5 2012.0

125 rows × 8 columns

In [ ]:
In [103]:
# Filter and display Disrupt year greater then 2013

disrupt_result[disrupt_result['Disrupt_year'] > 2013]
Out[103]:
Startup Product Funding Event Result OperatingState Funds_in_million Disrupt_year
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating 1.000 2016.0
13 Agrilyst agrilyst.com $1M Disrupt SF 2015 Winner Operating 1.000 2015.0
14 Aiden aiden.ai $750K Disrupt London 2016 Contestant Operating 0.750 2016.0
16 Aircall aircall.io $11.6M Disrupt SF 2015 Contestant Operating 11.600 2015.0
18 AirHelp airhelp.com $12.2M Disrupt NYC 2014 Contestant Operating 12.200 2014.0
... ... ... ... ... ... ... ... ...
633 Wellth wellthapp.com $2.5M Disrupt NY 2015 Contestant Operating 2.500 2015.0
634 Wellthy wellthy.com $2M Disrupt SF 2015 Contestant Operating 2.000 2015.0
635 Welltwigs welltwigs.com $50K Disrupt NY 2015 Contestant Operating 0.050 2015.0
642 Xendo xendo.com $28K Disrupt SF 2014 Contestant Acquired 0.028 2014.0
646 YayPay Inc yaypay.com $900K Disrupt London 2015 Contestant Operating 0.900 2015.0

140 rows × 8 columns

In [ ]:
In [104]:
# Filter and display Disrupt year greater then and equal to 2013

disrupt_result[disrupt_result['Disrupt_year'] >= 2013]
Out[104]:
Startup Product Funding Event Result OperatingState Funds_in_million Disrupt_year
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed 0.630 2013.0
3 3Dprintler 3dprintler.com $1M Disrupt NY 2016 Audience choice Operating 1.000 2016.0
13 Agrilyst agrilyst.com $1M Disrupt SF 2015 Winner Operating 1.000 2015.0
14 Aiden aiden.ai $750K Disrupt London 2016 Contestant Operating 0.750 2016.0
16 Aircall aircall.io $11.6M Disrupt SF 2015 Contestant Operating 11.600 2015.0
... ... ... ... ... ... ... ... ...
641 Workspot workspot.com $15.8M Disrupt NYC 2013 Contestant Operating 15.800 2013.0
642 Xendo xendo.com $28K Disrupt SF 2014 Contestant Acquired 0.028 2014.0
646 YayPay Inc yaypay.com $900K Disrupt London 2015 Contestant Operating 0.900 2015.0
656 Zenefits zenefits.com $583.6M Disrupt NYC 2013 Finalist Operating 583.600 2013.0
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating 3.400 2013.0

193 rows × 8 columns

In [ ]:
In [105]:
# Filter and display Disrupt year less then and equal to 2013

disrupt_result[disrupt_result['Disrupt_year'] <= 2013]
Out[105]:
Startup Product Funding Event Result OperatingState Funds_in_million Disrupt_year
1 3DLT 3dlt.com $630K Disrupt NYC 2013 Contestant Closed 0.63 2013.0
6 8 Securities 8securities.com $29M Disrupt Beijing 2011 Finalist Operating 29.00 2011.0
22 Alicanto alicanto.com $4M Disrupt SF 2012 Contestant Operating 4.00 2012.0
27 Amen. getamen.com $3M Disrupt SF 2011 Contestant Acquired 3.00 2011.0
34 Appbistro appbistro.com $600K Disrupt NYC 2010 Contestant Closed 0.60 2010.0
... ... ... ... ... ... ... ... ...
650 YourMechanic yourmechanic.com $28M Disrupt SF 2012 Winner Operating 28.00 2012.0
654 ZEFR zefr.com $62.1M Disrupt NYC 2010 Contestant Operating 62.10 2010.0
656 Zenefits zenefits.com $583.6M Disrupt NYC 2013 Finalist Operating 583.60 2013.0
660 Zula zulaapp.com $3.4M Disrupt SF 2013 Audience choice Operating 3.40 2013.0
661 Zumper zumper.com $31.5M Disrupt SF 2012 Finalist Operating 31.50 2012.0

178 rows × 8 columns